code: def perform_algorithm_seeding_test(hydra_overrides: Dict[str, str]):
      # Perform base run for comparison ----------------------------------------------------------------------------------
      base_dir = os.path.abspath(".")
      os.mkdir("./base_exp")
      os.chdir("./base_exp")
      # run training
      with Timeout(seconds=60):
          cfg = run_maze_job(
              hydra_overrides,
              config_module="maze.conf",
              config_name="conf_train",
          )
  
      # load tensorboard log
      tf_summary_files = glob.glob("*events.out.tfevents*")
      assert (
          len(tf_summary_files) == 1
      ), f"expected exactly 1 tensorflow summary file {tf_summary_files}"
      events_df = tensorboard_to_pandas(tf_summary_files[0])
      clear_global_state()
  
      # Perform comparison run with same seeds ---------------------------------------------------------------------------
      os.chdir(base_dir)
      os.mkdir("./exp_pos")
      os.chdir("./exp_pos")
  
      hydra_overrides[
          "seeding.agent_base_seed"
      ] = cfg.seeding.agent_base_seed
      hydra_overrides[
          "seeding.env_base_seed"
      ] = cfg.seeding.env_base_seed
      # run training
      with Timeout(seconds=60):
          run_maze_job(
              hydra_overrides,
              config_module="maze.conf",
              config_name="conf_train",
          )
  
      # load tensorboard log
      tf_summary_files = glob.glob("*events.out.tfevents*")
      assert (
          len(tf_summary_files) == 1
      ), f"expected exactly 1 tensorflow summary file {tf_summary_files}"
      events_df_2 = tensorboard_to_pandas(tf_summary_files[0])
      clear_global_state()
      del hydra_overrides["seeding.agent_base_seed"]
      del hydra_overrides["seeding.env_base_seed"]
  
      assert len(events_df) == len(events_df_2)
      for idx, (key, epoch) in enumerate(events_df.index):
          if "time" in key:
              continue
          assert (
              events_df_2.values[idx] == events_df.values[idx]
          ), f"Value not equal for key: {key} in epoch: {epoch}"
  
      # Perform second comparison run with different seeds ---------------------------------------------------------------
      os.chdir(base_dir)
      os.mkdir("./exp_neg")
      os.chdir("./exp_neg")
  
      # run training
      with Timeout(seconds=60):
          run_maze_job(
              hydra_overrides,
              config_module="maze.conf",
              config_name="conf_train",
          )
  
      # load tensorboard log
      tf_summary_files = glob.glob("*events.out.tfevents*")
      assert (
          len(tf_summary_files) == 1
      ), f"expected exactly 1 tensorflow summary file {tf_summary_files}"
      events_df_2 = tensorboard_to_pandas(tf_summary_files[0])
  
      all_equal = True
      for idx, (key, epoch) in enumerate(events_df.index):
          if "time" in key:
              continue
          all_equal = (
              all_equal
              and events_df.values[idx] == events_df_2.values[idx]
          )
      assert not all_equal, "The resulting logs should not be all equal"
  
file_path: maze/maze/test/train/seeding/test_algorithm_seeding.py
imports: import glob
  import os
  from typing import Dict
  import pytest
  from maze.test.shared_test_utils.run_maze_utils import run_maze_job
  from maze.utils.log_stats_utils import clear_global_state
  from maze.utils.tensorboard_reader import tensorboard_to_pandas
  from maze.utils.timeout import Timeout
label_desc: Use image node_id 2 for calling a global function with example usage: perform_algorithm_seeding_test(hydra_overrides) without return types